Working Papers

† For lognormally distributed parameters the coefficients of the underlying normal distribution are reported.
***, **, * indicate 1%, 5%, 10% significance level, respectively.
Overall, the model is highly significant, and the signs of the parameters for nonmonetary
attributes are generally as expected and also similar to the results found in the CE application
in Germany (see Oehlmann and Meyerhoff, 2016). Regarding the labelled alternative, the sign
and size of the alternative-specific constants (ASCs) indicate that respondents prefer electricity
generation from solar energy compared to the FSQ. Wind power is also valued positively, but
the parameter for this ASC is not statistically significant. In contrast, generating electricity from
biomass is clearly valued negatively compared to the FSQ. Respondents do not want to have
biomass production sites within 10 km of their place of residence.
Our results further indicate that people generally want REPS to be further from their place
of residence as opposed to near. This finding aligns with what many other studies concerned
with the externalities have found, especially for wind power (e.g., see Knapp and Ladenburg,
2015). With regard to the size of the REPS, the sign is positive but not statistically significant,
suggesting that the size of the sites is not an important issue for respondents. The number of
REPS, however, influences the probability of choosing an option positively. The share of the
landscape protected from the renewable energy development does not, at least on average,
systematically influence respondents’ choices. Finally, the probability of choosing an
alternative is significantly and positively influenced by building new high-voltage transmission
lines underground. In addition, relatively large and significant standard deviations, except for
number of REPS, indicate the presence of substantial unobserved preference heterogeneity.
Only in the case of the attribute indicating the number of renewable energy production sites do
preferences among respondents seem to be homogenous.
Turning to the main objectives of our analysis, we find both surcharge and rebate parameters
to be negative and statistically significant. Regarding the situation of electricity bill increase,
the negative sign indicates that the more expensive an alternative is, the less likely respondents
are to choose it. In the situation in which people would receive a rebate when they chose an
alternative, the negative sign indicates that smaller discounts also make it less likely that an
alternative is chosen. Additionally, the results indicate that, on average, marginal utility of
money is lower with a money gain than with a money loss.
Two parameters among the four interactions consisting of the two monetary variables, one
for the decreases and one for the increases, and the risk aversion and loss aversion parameters
are statistically significant. Loss aversion seems to play a role when people would get a rebate,
while risk aversion plays a role when people would have to pay a surcharge for a chosen
alternative. Starting with loss aversion, the parameter shows that the more people indicated that
they are loss averse in the lottery, the higher rebates they required before they would choose an
alternative labelled with wind, solar, or biomass instead of accepting the FSQ. Thus, people
who are more loss averse require more compensation before they accept externalities from
renewable electricity production. Regarding the influence of the recorded risk preferences with
respect to financial losses, the results suggest that people who are more risk averse are, on
average, willing to pay less for an alternative on the choice tasks. People who are more
concerned that an investment could lead to a loss of the invested money are, accordingly, less
prone to be willing to pay for changing the way REPS are built in their area.
To illustrate the differences in preferences between respondents with different levels of risk
and loss aversion, we simulate the mean WTP associated with our choice attributes for
respondents with different levels of and λ. Because our model allows marginal utility of
money to vary depending on whether alternatives in the choice sets present a surcharge or a
rebate to an electricity bill, it is also possible to calculate WTP with respect to income increase
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